Quantitative 18F-fluorocholine positron emission tomography for prostate cancer: correlation between kinetic parameters and Gleason scoring
© The Author(s). 2017
Received: 30 November 2016
Accepted: 21 February 2017
Published: 21 March 2017
The use of radiolabeled choline as a positron emission tomography (PET) agent for imaging primary tumors in the prostate has been evaluated extensively over the past two decades. There are, however, conflicting reports of its sensitivity and the relationship between choline PET imaging and disease staging is not fully understood. Moreover, relatively few studies have investigated the correlation between tracer uptake and histological tumor grade. This work quantified 18F-fluorocholine in tumor and healthy prostate tissue using pharmacokinetic modeling and stratified uptake parameters by histology grade. Additionally, the effect of scan time on the estimation of the kinetic exchange rate constants was evaluated, and the tracer influx parameters from full compartmental analysis were compared to uptake values quantified by Patlak and standardized uptake value (SUV) analyses.
18F-fluorocholine was administered as a 222 MBq bolus injection to ten patients with biopsy-confirmed prostate tumors, and dynamic PET data were acquired for 60 min. Image-derived arterial input functions were scaled by discrete blood samples, and a 2-tissue, 4-parameter model accounting for blood volume (2T4k+Vb) was used to perform fully quantitative compartmental modeling on tumor, healthy prostate, and muscle tissue. Subsequently, all patients underwent radical prostatectomy, and histological analyses were performed on the prostate specimens; kinetic parameters for tumors were stratified by Gleason score. Correlations were investigated between compartmental K 1 and K i parameters and SUV and Patlak slope; the effect of scan time on parameter bias was also evaluated.
Choline activity curves in seven tumors, eight healthy prostate regions, and nine muscle regions were analyzed. Net tracer influx was generally higher in tumor relative to healthy prostate, with the values in the highest grade tumors markedly higher than those in lower grade tumors. Influx terms from Patlak and full compartmental modeling showed good correlation within individual tissue groups. Kinetic parameters calculated from the entire 60-min scan data were accurately reproduced from the first 30 min of acquired data (R 2 ≈ 0.9).
Strong correlations were observed between K i and Patlak slope in tumor tissue, and K 1 and SUV were also correlated but to a lesser degree. Reliable estimates of all kinetic parameters can be achieved from the first 30 min of dynamic 18F-choline data. Although SUV, K 1, K i, and Patlak slope were found to be poor differentiators of low-grade tumor compared to healthy prostate tissue, they are strong indicators of aggressive disease.
KeywordsPET Choline Histology Prostate cancer Tracer kinetic modeling
Prostate cancer (PCa) is common in the USA and is the second leading cause of cancer death in men. In 2015, 220,800 American men were diagnosed and 27,540 are expected to die from the disease . Despite its prevalence, prognoses are generally good for this disease but accurate diagnoses are critical, as the therapeutic options depend on the aggressiveness and potential progression of the disease.
Clinically, most prostate cancers are detected first by a prostate-specific antigen (PSA) blood test or digital rectal exam and are confirmed through subsequent biopsy. Once confirmed, the patient will undergo diagnostic imaging procedures to assess the spread of the disease and plan treatment. Common imaging modalities include transrectal ultrasound, MRI, and PET/CT. MRI sessions generally consist of T1- and T2-weighted, DWI (diffusion-weighted imaging), and sometimes DCE (dynamic contrast enhanced) and spectroscopy sequences.
Molecular imaging with PET has several benefits due to the ability to target specific underlying biological processes like increased metabolism, upregulated protein and phospholipid synthesis, expression of androgen receptors and membrane proteins, or the osteoblastic reaction adjacent to metastases in the bone marrow . Clinically, routine PET imaging is performed with 18F-fluorodeoxyglucose, but this has shown limited utility for detecting PCa tumors [3, 4]. Improvements in tumor uptake have been reported for other tracers including 11C- and 18F-choline [3, 5–7], 11C-acetate [8–10], 11C-methionine [11, 12], and 18F-fluorodihydrotestosterone [13, 14]. However, these tracers have demonstrated relatively low specificity due to uptake in normal prostate tissue and benign lesions including prostatitis, high-grade intraepithelial neoplasia, or benign prostatic hyperplasia (BPH) [15, 16]. Consequently, recent focus has shifted to monitoring the expression of prostate-specific membrane antigen (PSMA), in which elevated levels have been histologically correlated with PCa tumor progression , androgen independence , and metastasis . PET agents have been developed as radiolabeled PSMA analogs like 68Ga-PSMA [20, 21] or the PSMA inhibitors 18F-DCFBC [22–24] and 18F-DCFPyL . These new, specific tracers, with high tumor to background contrast, are extremely promising and are being used more frequently, particularly for distant metastases and recurrence. Notwithstanding the growing interest in these novel tracers, 18F-Fluorodexoxyglucose (FDG) and choline are still the most clinically used PET tracers clinically for prostate cancer.
Choline has a strong uptake in the primary prostate tumor, even though it has not been shown to be highly specific, since benign prostatic hyperplasia also shows high uptake. Nevertheless, PET imaging of high choline uptake could be used to guide or support biopsy and surgery of primary prostate cancer. Biopsy procedures, especially of prostate tissue, can cause many issues including patient discomfort and infection. False negative results are also common, which may lead to a repeat biopsy and incorrect diagnosis. Incorporating imaging at an earlier point in the clinical routine can potentially improve patient management.
Radiolabeled choline has been used as a PET agent in humans for 20 years , and the prostate was among the first imaging targets . Over the years, many studies have been conducted investigating various aspects of its clinical utility including its efficacy for detecting primary tumors and systemic involvement [28, 29], preoperative staging and pelvic lymphadenopathy [30–32], post-therapy biochemical recurrence and correlations with PSA [33, 34], performance relative to FDG and other PET tracers [3, 35], MRI [36, 37] and MR spectroscopy , and quantitative evaluations of tracer kinetics in tissue [39–41]. Choline standardized uptake value (SUV) has been studied extensively, and there are many studies reporting the performance of choline for detecting patients with biopsy-confirmed prostate tumors, though with somewhat controversial results concerning the reported detection sensitivities [16, 42]. However, there are only a limited number of studies which have correlated choline uptake with histological tumor grade, in terms of histologic grading and Gleason score [28, 43, 44]. There are even fewer studies which have modeled the tracer kinetics and investigated those relationships with tissue grade  and none to our knowledge using the 18F-labeled derivative.
The aim of this study is to investigate the pharmacokinetics of 18F-fluoromethylcholine in various tissues, including pathology-confirmed primary prostate tumors and healthy prostate tissue, and perform a lesion-based correlation of quantified choline uptake with tumor grade. In particular, compartmental modeling was performed, and tissue parameters were stratified by Gleason score. Additionally, the relationships between tracer perfusion/extraction and influx compartmental rate constants were evaluated between SUV and Patlak linear regression analyses. The effect of scan time on kinetic parameter bias was also assessed.
While the immediate purpose of this work is to investigate the correlation of choline uptake kinetics with histologic tumor grade, it also provides the validation of methods that could be used with other tracers such as PSMA, which may offer an improved approach for characterizing and staging primary prostate cancer.
Ten men with biopsy-confirmed prostate cancer were recruited for the study. The mean age was 67 years (range, 59–72 years). Blood samples were not collected for one patient (patient 9), and so, he was not included in the kinetic analyses.
The imaging session included a PET/MR scan on the Biograph mMR (Siemens Medical Solutions Inc.). After the imaging session (18.7 ± 12.6 days), each patient underwent a radical prostatectomy and the excised prostate was sliced at 4–5 mm intervals before undergoing tissue processing. At each interval, thin sections of the paraffinized tissue slices were obtained by microtomy and stained for light microscopic assessment.
For PET/MR, subjects were positioned on the scanning bed, and a 60-min dynamic PET acquisition of the pelvis was started simultaneously with the intravenous injection of 222 MBq 18F-choline. The radiotracer was administered as a 1 mL bolus infused at a rate of 3 mL/s followed by a 20 mL saline flush. Venous blood samples were taken periodically over the course of the PET scan. The nurse’s visits into the scanning room to draw the blood were timed to accommodate the various MR sequences, so as to avoid potential field disturbances during the MR acquisitions. Efforts were made to sample the blood at least every 10 min.
The listmode PET data were divided into 30 frames of 12 × 10, 6 × 30, 5 × 60, 4 × 300, and 3 × 600 s duration, and each frame was reconstructed using an ordinary Poisson ordered subset expectation maximization (OP-OSEM) algorithm, with point spread function (PSF), with 3 iterations and 21 subsets. Attenuation correction was performed on the emission data using a multi-point T1-weighted DIXON sequence to derive the PET 511 keV attenuation map. The final image volume matrices of 172 × 172 × 127, with voxel size of 4.173 × 4.173 × 2.031, were post-smoothed with a 5-mm Gaussian filter.
Arterial input function
Here, A 0 is the original, uncorrected arterial blood curve, as measured in the images, and T is the unknown background tissue activity, approximated by a nearby region in muscle. The coefficients a 1 and a 2 correspond to spillover and partial volume, respectively. A least-squares fitting algorithm was employed to find the best estimates of these coefficients, while fitting the extracted blood curve data to the manual blood samples.
PET analyses and kinetic modeling
Regions of normal and focal tracer uptake were identified in the prostate and normal uptake in muscle tissue, and 30% threshold VOIs were drawn in the last frame of the dynamic series, i.e., images over 50–60 min. This threshold was found to provide acceptable delineation for all tumor foci, and if bladder expansion caused significant movement of the prostate, the VOIs were reregistered in each frame. The tissue time-activity data were weighted by frame length and fit using a 2-tissue model to estimate four compartmental rate parameters and blood volume fraction (V B). For the kinetic modeling and batch analyses, a combination of PMOD Kinetic Modeling Tool version 3.6 (PMOD Technologies Ltd.) and in-house developed software was used.
The diagnosis of prostatic adenocarcinoma in each case was made by application of well-known microscopic criteria . Prostatic adenocarcinoma can have a variety of microscopic patterns, and these patterns also contribute to the grade of the tumor. In general, prostatic adenocarcinoma tends to consist of malignant acini which are smaller than the adjacent benign acini, and which tend to infiltrate between these benign acini. The malignant acini may also have irregular or elongated contours and may fuse into cribriform structures or solid sheets of cells, among other patterns.
In addition to the routine clinical work-up, the pathologist was asked to follow a sector-based reporting scheme, consistent with the radiologist PI-RADS report. Tumor regions, with their associated Gleason score, were manually delineated within the anatomical map, facilitating the direct coregistration between regions of interest in PET and histopathology; this approach was similar to that used in previous works [40, 43, 44].
Focal 18F-choline uptake was identified in eight prostates, which were confirmed tumors by histology with 3 having Gleason score 3 + 4 (though one of these was from patient 9, without blood sampling, and was omitted from the analyses), 1 with 3 + 5, and 4 with 4 + 3. These and regions of healthy prostate tissue in eight patients were delineated with threshold VOIs. Tumors were not visualized in two patients by PET—histology reports showed the volumes of the largest tumors were less than 0.05 cm3 in both cases. Hence, the uptake trends of the tumors in these patients were not analyzed. For comparison to non-prostatic tissue, muscle activity curves were also measured for all patients.
2T4k+Vb model rate parameter values (with %SE) in muscle, healthy prostate, and tumor tissue
Various metrics (with %SE) from PET analyses of healthy prostate and tumor tissue
3 + 4
4 + 3
4 + 3
4 + 3
3 + 5
4 + 3
3 + 4
For all tissue groups, the data showed that choline influx parameters, calculated with 30 min of dynamic data, were significantly well correlated with those of the full 60 min.
This work investigated 18F-choline uptake patterns in prostatic tumors, healthy prostate tissue, and muscle; tracer kinetic analyses were performed with compartmental modeling. Plasma input functions were obtained through image-derived methods, fit to manual blood measurements while accounting for partial volume and spillover effects, and corrected for plasma partitioning and metabolites. Correlations were evaluated between the influx rate parameter calculated from fully quantitative analyses and Patlak and 60-min SUV.
A limitation of this study is that arterial blood was not collected from the subjects, so accurate measurements of the radiolabeled metabolites were not possible. Hence, we applied a single metabolite correction to all plasma curves. This method, however, is not ideal, since high interpatient variability in the parent tracer fraction has been reported  which could lead to errors and bias in the estimations of the rate parameters. To characterize these effects, we systematically ran the curve fitting algorithm on all tissue data with different metabolite corrections [41, 47, 50] and corresponding plasma inputs. We found that there were indeed rather large variances on some of the estimations of the individual microparameters K 1–k 4, but that the calculations of the macroflux parameter K i were robust. A similar investigation was performed to assess the effect of the plasma partitioning. For the first four patients with manual blood sampling, the kinetic parameters estimated using the linear partitioning method used here were compared to those estimated using the mean of the individual patient measurements as a constant partitioning ratio. Again, we found that the estimates of the individual compartmental rate parameters were far more sensitive to this than the calculated tracer influx. Plots showing the results of both of these tests are shown in Additional file 1: Figures S2 and S3.
The parameters that were evaluated to characterize choline uptake in tissue were SUV, K 1, K i, and Patlak slope. The most relevant result of this study is that SUV, K 1, K i, and Patlak slope are indeed strong indicators of aggressive tumor in the prostate. As can be seen in Fig. 5, while all three parameters show limited correlation with Gleason score for low-grade tumors, they all show a dramatic increase at Gleason score 4 + 3. This could suggest that SUV, and even better K i and Patlak slope, could be used as indicators of aggressive tumors. Microparameters k 2, k 3, and k 4 showed no correlation with Gleason score (results not reported). However, it is possible that these kinetic parameters could be more helpful in PET imaging with tracers other than choline.
It is well known that choline is rapidly transported and phosphorylated within the cells , and indeed, we observed that the majority of the tissue tracer uptake occurred within the first 5–10 min. Hence, this work investigated the effects of scan time reduction on the quantitative analyses. Here, we found that the calculation variability in the microparameters k 2–k 4 was sensitive to the scan time but estimates of perfusion and choline transport, represented by K 1, and the macroflux parameter K i were more stable; this finding is not uncommon for compartmental modeling tasks. This notwithstanding, accurate characterizations of 18F-choline influx for all tissue could be quantified using 30 min of dynamic scan data, as can be appreciated in Figs. 8 and 9. Further, the K i correlations we found were comparable to those reported by Verwer et al. for scan durations 40 vs. 10 min, 40 vs. 20 min, and 40 vs. 30 min. Our correlation slopes for the same times were 1.2, 1.1, and 0.99 and those from Verwer et al. were 0.93, 1.05, and 1.03. Our R 2 values were 0.93, 0.95, and 1, and those from Verwer et al. were 0.97, 0.9, and 1. These findings may prove useful for future studies investigating dynamic 18F-choline PET for prostate applications, since scan time should be kept to a minimum for patient convenience and scanner throughput considerations and also to avoid bladder filling issues (which is not a consideration for the 11C-labeled derivative). Motion is not typically a major problem with pelvic scanning, but expansion of the bladder can cause deformation of the internal structures, affecting the voxel measurements over time. Shorter scan times would minimize these effects.
An important result of this work is the development of a method and a protocol to perform kinetic studies of prostate cancer tracer in primary tumor and perform correlation between compartmental model parameters and Gleason score. We plan to apply these methods with other newer and more specific tracers for prostate cancer, with the purpose of correlating aggressiveness of the disease and PET imaging parameters, therefore providing a non-invasive method for supporting staging and therapy planning of primary tumor.
This study of the kinetics of 18F-choline, using both Patlak method and fully compartmental modeling, showed a strong correlation between K i and Patlak slope in tumor tissue; K 1 and SUV were also correlated to a lesser degree. Due to differences in 18F-choline uptake profiles for different tissues, a 2-tissue model was needed to quantify tracer uptake. Fully quantitative analyses were performed for accurate characterization of the tissue uptake, but this work showed that reliable estimates of all kinetic parameters can be achieved with only 30 min of dynamic data. Finally, an interesting result of this study is that even if SUV, K 1, K i, and Patlak slope are poor differentiators of low-grade tumor compared to healthy prostate tissue, they are strong indicators of aggressive disease: they all dramatically increase at Gleason score 4 + 3. The good correlation between K i as determined from the compartment model and from the Patlak analyses indicates that the Patlak plot can be used as a substitute for full compartmental analysis of the 18F-choline data in the prostate.
Compartmental model using 2-tissues and 4 rate + blood volume parameters
Akaike information criterion
Benign prostatic hyperplasia
Dynamic contrast enhanced
Domain specific review board
Hematoxylin and eosin
Ordinary Poisson ordered subset expectation maximization
Positron emission tomography
Point spread function
Prostate-specific membrane antigen
Standardized uptake value
- VB :
Blood volume fraction
Volume of interest
This study was funded by the National University Cancer Institute, Singapore Centre Grant Seed Funding Program
JS analyzed the PET data and wrote the manuscript. ZW and MS assisted with the imaging protocol. SR facilitated the correlation of the imaging and histologic registration. MC was integral in developing the study concept and reviewed the manuscript. LE assisted with the analyses and reviewed the manuscript. DT gave advice in the data interpretation and reviewed the manuscript. TT performed the histologic analyses. EC recruited the patients and reviewed the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Ethics approval and consent to participate
This study was approved by the Domain Specific Review Board (DSRB) of the National University Hospital Singapore, and all the subjects signed an informed consent to participate.
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